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arXiv · 2307.11522

Semantically-enhanced Deep Collision Prediction for Autonomous Navigation using Aerial Robots

Mihir Kulkarni·Huan Nguyen·Kostas Alexis
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Abstract

This paper contributes a novel and modularized learning-based method for aerial robots navigating cluttered environments containing hard-to-perceive thin obstacles without assuming access to a map or the full pose estimation of the robot. The proposed solution builds upon a semantically-enhanced Variational Autoencoder that is trained with both real-world and simulated depth images to compress the input data, while preserving semantically-labeled thin obstacles and handling invalid pixels in the depth sensor's output. This compressed representation, in addition to the robot's partial state involving its linear/angular velocities and its attitude are then utilized to train an uncertainty-aware 3D Collision Prediction Network in simulation to predict collision scores for candidate action sequences in a predefined motion primitives library. A set of simulation and experimental studies in cluttered environments with various sizes and types of obstacles, including multiple hard-to-perceive thin objects, were conducted to evaluate the performance of the proposed method and compare against an end-to-end trained baseline. The results demonstrate the benefits of the proposed semantically-enhanced deep collision prediction for learning-based autonomous navigation.

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Mihir Kulkarni, Huan Nguyen, Kostas Alexis. 2023-07-21. Semantically-enhanced Deep Collision Prediction for Autonomous Navigation using Aerial Robots. https://arxiv.org/abs/2307.11522

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